Choosing an AI agent framework should never feel like a gamble. Pick well and you’ll ship faster, control costs, and avoid flaky behavior; pick poorly and you’ll spend weeks firefighting brittle chains. This practical guide distills what a head engineer would tell the team after months of hands-on trials—so you can select your AI agent framework and supporting tools with confidence.
Engineer’s rule: start from your workload and reliability needs, then add an orchestrator for state, a sensible vector store, a serving strategy (managed or self-hosted), and observability/evals from day one.
AI agent framework decision tree (60 seconds)
- Where will it run?
- Strict/on-prem: prefer open-source AI agent framework + self-hosted serving (vLLM) + open vector DB (Qdrant, Milvus, pgvector).
- Cloud OK: managed LLMs + managed vector (Pinecone, Weaviate) to ship quickly.
- Primary workload?
- RAG / document QA / enterprise search: LlamaIndex or LangChain (often with LangGraph for reliability).
- Multi-agent collaboration and tool use: CrewAI or AutoGen / Microsoft Agent Framework.
- Microsoft/.NET: Semantic Kernel + Azure OpenAI + Azure AI Search or pgvector.
- Do you need durable control flow (state, retries, approvals)?
- If yes, add LangGraph alongside your AI agent framework.
- Throughput and cost?
- High-throughput/self-hosted: vLLM.
- Local/offline/dev: Ollama.
- Measurement from day one?
- Tracing/observability (LangSmith, Arize Phoenix) + evaluations (Ragas, Promptfoo, DeepEval).
What “great” looks like when you pick an AI agent framework
- Task fit: Does the AI agent framework excel at your main job (RAG, multi-agent, .NET enterprise, realtime)?
- Reliability: State machines/graphs, resumability, timeouts, human-in-the-loop checkpoints.
- Ecosystem: Connectors, tool/function calling, deployment surfaces, active community.
- Observability and evals: Tracing, datasets, A/Bs, guardrails for reliable JSON outputs.
- Performance and cost: Latency, throughput, caching, quantization, predictable unit economics.
- Governance: Secrets hygiene, PII redaction, RBAC, auditability, regional controls.
- Team fit: Preferred language/runtime, learning curve, documentation and examples.
Framework profiles (choose with confidence)
| Criterion | LangChain | LangGraph | LlamaIndex | CrewAI | AutoGen / Agent Framework | Semantic Kernel |
|---|---|---|---|---|---|---|
| RAG strength | 9 | 8 | 10 | 6 | 7 | 8 |
| Multi-agent ergonomics | 7 | 9 | 7 | 9 | 9 | 8 |
| Reliability / stateful flows | 7 | 10 | 8 | 8 | 8 | 8 |
| Ecosystem & integrations | 10 | 9 | 9 | 7 | 8 | 8 |
| .NET/Enterprise fit | 6 | 7 | 7 | 6 | 8 | 10 |
| Learning curve (lower=easier) | 7 | 8 | 6 | 6 | 7 | 7 |
LangChain + LangGraph (Python/JS)
Choose if: you want mainstream patterns (prompts → tools → RAG) with massive integrations, plus LangGraph for durable, stateful flows.
Why it works: LangChain’s Runnables are flexible; LangGraph adds checkpoints, retries, timeouts, and human approvals—turning a good AI agent framework into a reliable production system.
Watchouts: Keep chains explicit and instrument with tracing to avoid silent failures.
LlamaIndex (Python/TS)
Choose if: RAG is central and you care about loaders, indexing strategies, query engines (including graph-RAG), and retrieval tuning.
Why it works: Purpose-built RAG AI agent framework with excellent ingestion and configurability.
Watchouts: For complex multi-step flows, pair with an orchestrator for state.
CrewAI (Python)
Choose if: you want an approachable multi-agent model (roles → tasks → tools → memory) with a quick path to collaboration between specialized agents.
Why it works: Clear ergonomics; faster to model multi-agent work than assembling it all by hand.
Watchouts: Still apply guardrails (allow-listed tools, validated JSON outputs).
AutoGen / Microsoft Agent Framework
Choose if: you’re in the Microsoft ecosystem, or you like AutoGen’s collaboration patterns with an enterprise-ready runtime.
Why it works: A unifying AI agent framework direction from Microsoft that blends AutoGen ergonomics with Semantic Kernel integrations.
Watchouts: Track versioning and migration notes as the SDK/runtime evolves.
Semantic Kernel (.NET / Python / JS)
Choose if: you’re a .NET/Azure shop and want planners/skills with first-party Azure integrations.
Why it works: Model-agnostic SDK with enterprise governance and Azure-native services.
Watchouts: Use Azure AI Search or pgvector to keep retrieval straightforward.
Serving the model: managed vs self-hosted
- Managed LLMs (OpenAI, Azure, Anthropic, etc.) are fastest to production; they come with strong tooling and pay-as-you-go economics.
- Self-hosted gives control and predictable unit costs:
- vLLM: high throughput, OpenAI-compatible server mode; great when you own SLAs.
- Ollama: simplest local runs; ideal for prototyping and offline demos.
Engineer’s rule: prototype managed; if you need to own latency/cost, benchmark vLLM early.
Retrieval and memory: vector databases you won’t regret
- Managed: Pinecone, Weaviate Cloud – fast start, SLAs, hybrid search when you need it.
- Open/self-hosted: Qdrant (Rust), Milvus (scale), pgvector (Postgres extension), FAISS (in-process library).
Simple rule of thumb:
- Already on Postgres? Start with pgvector.
- Want managed speed? Pinecone.
- Need OSS control at scale? Qdrant or Milvus.
Observability and evaluations (don’t ship blind)
- Tracing/monitoring:
- LangSmith: datasets, runs, regressions; framework-agnostic tracing.
- Arize Phoenix: open-source observability with OpenTelemetry integration.
- Automated evals:
- Ragas: RAG metrics like context precision/recall and faithfulness.
- Promptfoo: CLI/CI for prompts and red-teaming.
- DeepEval: unit-test-style checks with LLM-as-judge metrics.
Minimum viable discipline: wire tracing and a tiny golden dataset on day one. Evals catch regressions when prompts, models, or tools change.
Reference stacks
1) Production RAG over docs/Notion/SharePoint
- AI agent framework: LlamaIndex (RAG) + selected LangChain utilities
- Orchestrator: LangGraph (durable state, retries, human-in-the-loop)
- Vector DB: Pinecone (managed) or Qdrant (self-hosted)
- Serving: Managed LLM (fast) or vLLM (self-hosted)
- Observability/Evals: LangSmith + Ragas
Why this works: clean ingestion, configurable retrieval, and a battle-tested control layer.
2) Multi-agent research and tool use (browser/code)
- AI agent framework: CrewAI or AutoGen / Microsoft Agent Framework
- Orchestrator: LangGraph for timeouts/checkpoints
- Serving: Managed LLM for speed; Ollama for local prototyping
- Observability/Evals: Arize Phoenix + Promptfoo (add red-team tests)
3) .NET enterprise assistant (compliance-first)
- AI agent framework: Semantic Kernel (.NET)
- Model & search: Azure OpenAI + Azure AI Search or pgvector
- Observability/Evals: LangSmith or Phoenix + Ragas
4) Self-hosted, cost-tight
- Serving: vLLM
- Vector: pgvector or Qdrant
- Orchestrator: LangGraph
Why: predictable costs + good throughput + simple ops.
Practical checklist
- Define your primary workload (RAG, multi-agent, .NET, self-hosted).
- Pick the AI agent framework that matches it (use the decision tree).
- Add an orchestrator (LangGraph) if you need state/retries/human-in-the-loop.
- Choose a vector store (Pinecone/Weaviate vs Qdrant/Milvus/pgvector).
- Decide serving (managed vs vLLM/Ollama); enable caching/quantization.
- Wire tracing (LangSmith/Phoenix) and evals (Ragas/Promptfoo/DeepEval).
- Add guardrails (validated JSON outputs, tool allow-lists, content filters).
- Write a runbook (fallback models, rate limits, escalation to human).
FAQ about the AI agent framework
What is an AI agent framework?
An AI agent framework provides building blocks for LLM apps—prompting, tool use, retrieval, and orchestration—plus integrations for storage and serving.
Which AI agent framework is best for RAG?
LlamaIndex (indexing and query engines) or LangChain with LangGraph when reliability and stateful flows matter.
Do I need LangGraph if I use LangChain?
If you have multi-step or background workflows, or need human approvals, yes—LangGraph adds state, retries, and resumability.
Which vector DB should I pick?
Pinecone/Weaviate for managed speed; Qdrant/Milvus for open-source control; pgvector if you already run Postgres.
Ollama vs vLLM?
Ollama for local dev/offline tests; vLLM for high-throughput self-hosted serving.
Suggested internal links
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Conclusion
You now have a repeatable, defensible way to choose the right AI agent framework and tools. Next, we’ll set up the development environment for your chosen stack—install SDKs, configure keys, enable tracing and evaluations—and run a hello-world agent end to end in Chapter 3: Setting Up Your Development Environment.
👉 Begin Chapter 3 and Set Up Your Development Environment
Key External Resources
Frameworks & Orchestrators
- LangChain —
https://python.langchain.com/ - LangGraph —
https://langchain-ai.github.io/langgraph/ - LlamaIndex —
https://docs.llamaindex.ai/ - CrewAI —
https://docs.crewai.com/ - Microsoft Agent Framework / AutoGen —
https://github.com/microsoft/autogenandhttps://microsoft.github.io/autogen/ - Semantic Kernel —
https://learn.microsoft.com/semantic-kernel/
Model Serving
- vLLM —
https://docs.vllm.ai/ - Ollama —
https://ollama.com/
